text: import torch
  
  def train(model, iterator, optimizer, criterion, clip):
      
      model.train()
      
      epoch_loss = 0
      
      for i, batch in enumerate(iterator):
          
          src = batch.src
          trg = batch.trg
          
          optimizer.zero_grad()
          
          output = model(src, trg)
          
          #trg = [trg len, batch size]
          #output = [trg len, batch size, output dim]
          
          output_dim = output.shape[-1]
          
          output = output[1:].view(-1, output_dim)
          trg = trg[1:].view(-1)
          
          #trg = [(trg len - 1) * batch size]
          #output = [(trg len - 1) * batch size, output dim]
          
          loss = criterion(output, trg)
          
          loss.backward()
          
          torch.nn.utils.clip_grad_norm_(model.parameters(), clip)
          
          optimizer.step()
          
          epoch_loss += loss.item()
          
      return epoch_loss / len(iterator)